Papers
arxiv:2602.22839

DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation

Published on Feb 26
· Submitted by
Zheng Hao
on Mar 9
Authors:
,
,
,
,
,
,
,
,

Abstract

DeepPresenter is an agentic framework for presentation generation that adaptively plans and refines slide artifacts through environment-grounded reflection, achieving state-of-the-art performance with reduced computational costs.

Presentation generation requires deep content research, coherent visual design, and iterative refinement based on observation. However, existing presentation agents often rely on predefined workflows and fixed templates. To address this, we present DeepPresenter, an agentic framework that adapts to diverse user intents, enables effective feedback-driven refinement, and generalizes beyond a scripted pipeline. Specifically, DeepPresenter autonomously plans, renders, and revises intermediate slide artifacts to support long-horizon refinement with environmental observations. Furthermore, rather than relying on self-reflection over internal signals (e.g., reasoning traces), our environment-grounded reflection conditions the generation process on perceptual artifact states (e.g., rendered slides), enabling the system to identify and correct presentation-specific issues during execution. Results on the evaluation set covering diverse presentation-generation scenarios show that DeepPresenter achieves state-of-the-art performance, and the fine-tuned 9B model remains highly competitive at substantially lower cost. Our project is available at: https://github.com/icip-cas/PPTAgent

Community

Paper author Paper submitter

This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

The following papers were recommended by the Semantic Scholar API

Please give a thumbs up to this comment if you found it helpful!

If you want recommendations for any Paper on Hugging Face checkout this Space

You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend

Slide 1 — Title

KRISHIMITRA AI
AI-Powered Agriculture & Direct Farmer-to-Buyer Marketplace

“Grow Smarter. Sell Directly. Earn Better.”

Project Team: [pixel punks]
BCA – Computer Applications
College :- GSS Belagavi


Slide 2 — Problem Statement

THE PROBLEM

Farmers often depend on multiple middlemen.

A significant portion of the final selling value may go through intermediaries.

Farmers have limited direct access to companies and bulk buyers.

Difficulty in deciding which crop to grow.

Crop diseases can reduce yield and income.

Agricultural information is not easily accessible to every farmer.

Traditional Flow:
Farmer → Trader → Wholesaler → Distributor → Company/Consumer


Slide 3 — Our Solution

KRISHIMITRA AI — OUR SOLUTION

A single digital platform connecting:

FARMER ↔ AI AGRICULTURAL ASSISTANCE ↔ MARKETPLACE ↔ BUYER/COMPANY

Core Features

AI Crop Recommendation

AI Crop Disease Detection

AI Agricultural Assistant

Direct Farmer-to-Buyer Marketplace

Product Image Upload & Management

Quantity-Based Ordering

Multilingual Support

Farmer & Company Profiles


Slide 4 — Farmer Features

FARMER MODULE

👨‍🌾 Farmer can:

Create and manage profile

Add farm details

Enter soil, water and land information

Get AI crop recommendations

Upload crop image for disease detection

Ask agricultural questions to AI assistant

Upload agricultural products

Add product image, quantity and price

Receive buyer orders

Manage listed products


Slide 5 — Direct Marketplace

FARMER-TO-BUYER MARKETPLACE

Without KrishiMitra

Farmer → Middleman → Wholesaler → Company

With KrishiMitra

Farmer → KrishiMitra → Company/Buyer

Farmer

Upload product image

Product name

Available quantity

Expected price

Product details

Buyer

Search products

View farmer & product

View product image

Select required quantity

Confirm order

Goal: Better market access and reduced unnecessary intermediary dependency.


Slide 6 — AI Crop Planner

AI CROP RECOMMENDATION

Input:

Location

Soil type

Soil pH

Water availability

Land area

Budget

Previous crops

Season

Soil report (optional)

Python + Machine Learning Model

Output:

Suitable crop recommendations

Soil compatibility

Water requirement

Growing season

Basic farming guidance

Example:
Rice → Maize → Groundnut


Slide 7 — AI Disease Detection

AI CROP DISEASE DETECTION

📷 Upload / Capture Crop Image

Image Processing

AI / Computer Vision Model

Disease Classification

Result

Detected crop/plant

Possible disease

Confidence score

Symptoms

General preventive guidance

Recommended next steps

Technologies: Python + OpenCV/Pillow + ML/DL


Slide 8 — AI Agricultural Assistant

AI AGRICULTURAL ASSISTANT

Farmers can ask:

What crop should I grow?

What disease is affecting my crop?

What conditions does this crop need?

What should I do after disease detection?

How can I improve my farming practices?

How can I sell my products?

Multilingual Support

English | Kannada | Hindi | Marathi

Future

Voice Input

Voice Output

WhatsApp AI Assistant


Slide 9 — Technology Stack

TECHNOLOGY STACK

Frontend

Next.js

UI

Routing

Dashboard

Marketplace

API Integration

TypeScript

Type safety

Reliable and maintainable code

JavaScript

Interactive UI

Client-side logic

Backend / Database

PocketBase

Authentication

Database

File/Image Storage

APIs

User Management

AI / ML

Python

AI & data processing

Scikit-learn

Machine Learning

Training

Classification

Prediction

OpenCV / Pillow

Image processing


Slide 10 — System Architecture

KRISHIMITRA AI — SYSTEM ARCHITECTURE

FARMER / BUYER

NEXT.JS APPLICATION
TypeScript + JavaScript

POCKETBASE
┌────────┼────────┐
↓ ↓ ↓
Users Products Orders
↓ ↓ ↓
Images Farmer Data

PYTHON AI SERVICES

┌─────────┴─────────┐
↓ ↓
Crop Recommendation Disease Detection
↓ ↓
└─────────┬─────────┘

AI RESULT / GUIDANCE


Slide 11 — Complete User Flow

FROM FARM TO MARKET

Register

Create Farmer Profile

Enter Farm & Soil Details

AI Crop Recommendation

Grow Crop

AI Disease Detection

Harvest

Upload Product + Image

Buyer Discovers Product

Buyer Selects Required Quantity

Confirm Order

Direct Procurement

KrishiMitra connects the agricultural lifecycle from decision-making to selling.


Slide 12 — Impact & Future Scope

IMPACT & FUTURE SCOPE

Expected Impact

Better agricultural decision-making

Earlier disease identification

Improved market access

Direct connection with buyers

Transparent product information

Reduced unnecessary intermediary dependency

Future Scope

AI yield prediction

Crop price prediction

Weather alerts

IoT soil sensors

Smart irrigation

Digital payments

Logistics integration

Government scheme integration

Voice-based agricultural assistant

WhatsApp integration

Bulk procurement & bidding


Slide 13 — Conclusion

KRISHIMITRA AI

“Grow Smarter. Detect Earlier. Sell Directly.”

KrishiMitra combines:

AI + Agriculture + Marketplace + Technology

to help farmers:

🌱 Make Better Farming Decisions
📷 Detect Crop Problems Earlier
🏪 Reach Buyers Directly
💰 Improve Market Opportunities

Technology

Next.js + TypeScript + JavaScript + PocketBase + Python + Scikit-learn + Computer Vision

THANK YOU
Questions & Answers

Sign up or log in to comment

Get this paper in your agent:

hf papers read 2602.22839
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper 2

Datasets citing this paper 1

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2602.22839 in a Space README.md to link it from this page.

Collections including this paper 2